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36th International Florida Artificial Intelligence Research Society Conference, FLAIRS-36 2023

作者： Christoforou, Christoforos Papadopoulos, Timothy C. Theodorou, Maria Division of Computer Science Mathematics and Science St. John’s University NY United States Department of Psychology & Center for Applied Neuroscience University of Cyprus Nicosia Cyprus

Children’s performance on the spoonerism task, a behavioral test that measures phonological processing skills, predicts reading abilities and related disorders. However, this rela-tionship between phonological processing skills and dyslexia has been primarily examined based on behavioral responses to the spoonerism task. As a result, there is a growing interest in developmental neuroscience to explore the neural origins of this relationship and its relation to reading difficulties. Yet, traditional electroencephalography (EEG) analysis methods had little success identifying informative neural components that depict neural differences in children with reading disorders during spoonerism. The current study explores a novel computational approach to isolate informative neural signatures elicited during the spoonerism test. We apply our method to EEG data obtained from a group of children with dyslexia and controls during the execution of a spoonerism task. Our findings demonstrate that our method extracts components that characterize the neural origins of complex cognitive phonological processes, explains differences between children with dyslexia and controls, and generates novel insights into the neural underpinnings of dyslexia in children. © 2023 by the authors. All rights reserved.

关键词： Electroencephalography

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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applied mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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Preventing Pressure Oscillations Does Not Fix Local Linear Stability Issues of Entropy-Based Split-Form High-Order Schemes

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Communications on applied mathematics and Computation 2022年第3期4卷 880-903页

作者： Hendrik Ranocha Gregor J.Gassner Applied Mathematics Münster University of Münster48149 MünsterGermany Computer Electrical and Mathematical Science and Engineering(CEMSE)Division King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Department of Mathematics and Computer Science Center for Data and Simulation ScienceUniversity of CologneCologneGermany

Recently,it was discovered that the entropy-conserving/dissipative high-order split-form discontinuous Galerkin discretizations have robustness issues when trying to solve the sim-ple density wave propagation example for the compressible Euler *** issue is related to missing local linear stability,i.e.,the stability of the discretization towards per-turbations added to a stable base *** is strongly related to an anti-diffusion mech-anism,that is inherent in entropy-conserving two-point fluxes,which are a key ingredi-ent for the high-order discontinuous Galerkin *** this paper,we investigate if pressure equilibrium preservation is a remedy to these recently found local linear stability issues of entropy-conservative/dissipative high-order split-form discontinuous Galerkin methods for the compressible Euler *** equilibrium preservation describes the property of a discretization to keep pressure and velocity constant for pure density wave *** present the full theoretical derivation,analysis,and show corresponding numerical results to underline our *** addition,we characterize numerical fluxes for the Euler equations that are entropy-conservative,kinetic-energy-preserving,pressure-equilibrium-preserving,and have a density flux that does not depend on the *** source code to reproduce all numerical experiments presented in this article is available online(https://***/10.5281/zenodo.4054366).

关键词： Entropy conservation Kinetic energy preservation Pressure equilibrium preservation Compressible Euler equations Local linear stability Summation-by-parts

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Automated Ensemble Multimodal Machine Learning for Healthcare

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4213-4226页

作者： Imrie, Fergus Denner, Stefan Brunschwig, Lucas S. Maier-Hein, Klaus Van Der Schaar, Mihaela University of Oxford Department of Statistics Oxford United Kingdom Heidelberg University Faculty of Mathematics and Computer Science Germany École Polytechnique Fédérale de Lausanne Switzerland Division of Medical Image Computing Germany Heidelberg University Hospital Pattern Analysis and Learning Group Department of Radiation Oncology Germany Heidelberg Germany University of Cambridge Department of Applied Mathematics and Theoretical Physics United Kingdom

The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. This stands in stark contrast with clinician decision-making which employs diverse information from multiple sources. While several multimodal machine learning approaches exist, significant challenges in developing multimodal systems remain that are hindering clinical adoption. In this paper, we introduce a multimodal framework, AutoPrognosis-M, that enables the integration of structured clinical (tabular) data and medical imaging using automated machine learning. AutoPrognosis-M incorporates 17 imaging models, including convolutional neural networks and vision transformers, and three distinct multimodal fusion strategies. In an illustrative application using a multimodal skin lesion dataset, we highlight the importance of multimodal machine learning and the power of combining multiple fusion strategies using ensemble learning. We have open-sourced our framework as a tool for the community and hope it will accelerate the uptake of multimodal machine learning in healthcare and spur further innovation. © 2013 IEEE.

关键词： Adversarial machine learning

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Set-based Particle Swarm Optimization for Data Clustering: Comparison and Analysis of Control Parameters 23

Set-based Particle Swarm Optimization for Data Clustering: C...

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7th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2023

作者： de Wet, Rijk Marius Engelbrecht, Andries Department of Industrial Engineering Stellenbosch University Stellenbosch South Africa Computer Science Division Stellenbosch University Stellenbosch South Africa Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Kuwait

ISBN: (纸本)9781450399920

Data clustering is a highly studied field of data science and computational intelligence. Population-based algorithms such as particle swarm optimization (PSO) have shown to be effective at data clustering. Set-based particle swarm optimization (SBPSO) is a generic set-based PSO variant that has shown promise in clustering stationary and non-stationary data. In this paper, SBPSO is used to cluster fifteen datasets with diverse characteristics. The clustering ability of SBPSO is compared in depth to the performance of six other tuned clustering algorithms. A sensitivity analysis of the SBPSO control parameters is performed to determine the effect that variation in these control parameters have on swarm diversity and other measures. SBPSO ranked third from among the algorithms evaluated and proved a viable clustering algorithm. A trade-off between swarm diversity and clustering ability was discovered, and the control parameters that control this trade-off were determined. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Sensitivity analysis

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Hummingbird-Optimizer and Otsu/ Kapur-Assisted Segmentation of Foot Ulcer 1st

Hummingbird-Optimizer and Otsu/ Kapur-Assisted Segmentation ...

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1st International Conference on Data Engineering and Machine Intelligence, ICDEMI 2023

作者： Mohan, Ramya Kadry, Seifedine Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates

ISBN: (纸本)9789819776153

In addition to traditional approaches, several computerized techniques have been developed to enhance the results. The automation in the medical domain considerably reduced the burden and improved disease diagnosis, treatment planning, and implementation. With the advent of automation, the medical domain has greatly reduced the burden of diagnosing diseases, developing treatment plans, and implementing them. This research aims to develop image-processing assisted detection of Diabetic Foot Ulcer (DF) from digital photographs. Digital photographs are used to assist in diagnosing Diabetic Foot Ulcer (DF). As part of the developed tool, (i) images are collected and resized, (ii) Otsu/Kapur and Hummingbird-optimizer thresholding is supported, (iii) level-set segmentation is used to extract the DF region, and (iv) the developed scheme is compared with the binary mask and its performance is verified. A total of 300 images are examined in this work, and MATLAB software is used to conduct the experiments. Comparing Otsu + LS with Kapur + LS, the overall result achieved with Kapur’s enhanced image is better than that of Otsu + LS. Authentic clinical DF images can be used to verify the proposed scheme in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： MATLAB

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Neural Bayes estimators for censored inference with peaks-over-threshold models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 19070-19118页

作者： Jordan Richards Matthew Sainsbury-Dale Andrew Zammit-Mangion Raphaël Huser School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh Edinburgh UK and Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia and School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.

关键词： censored data convolutional neural network likelihood-free inference neural estimation spatial extremes threshold exceedance

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Local Sequential MCMC for Data Assimilation with Applications in Geoscience

arXiv

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arXiv 2024年

作者： Ruzayqat, Hamza Knio, Omar Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

This paper presents a new data assimilation (DA) scheme based on a sequential Markov Chain Monte Carlo (SMCMC) DA technique [36] which is provably convergent and has been recently used for filtering, particularly for high-dimensional non-linear, and potentially, non-Gaussian state-space models. Unlike particle filters, which can be considered exact methods and can be used for filtering non-linear, non-Gaussian models, SMCMC does not assign weights to the samples/particles, and therefore, the method does not suffer from the issue of weight-degeneracy when a relatively small number of samples is used. We design a localization approach within the SMCMC framework that focuses on regions where observations are located and restricts the transition densities included in the filtering distribution of the state to these regions. This results in immensely reducing the effective degrees of freedom and thus improving the efficiency. We test the new technique on high-dimensional (d ∼ 104 − 105) linear Gaussian model and non-linear shallow water models with Gaussian noise with real and synthetic observations. For two of the numerical examples, the observations mimic the data generated by the Surface Water and Ocean Topography (SWOT) mission led by NASA, which is a swath of ocean height observations that changes location at every assimilation time step. We also use a set of ocean drifters' real observations in which the drifters are moving according the ocean kinematics and assumed to have uncertain locations at the time of assimilation. We show that when higher accuracy is required, the proposed algorithm is superior in terms of efficiency and accuracy over competing ensemble methods and the original SMCMC *** Codes 62M20, 60G35, 60J20, 94A12, 93E11, 65C40 © 2024, CC BY.

关键词： Markov chains

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Novel characterization and tuning methods for integrating processes

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1387-1395页

作者： Jorge, Cárdenas-Cabrera Jorge, Henríquez-Novoa Wendell, Martínez-Rodríguez Edalatpanah, S.A. Shariq, Aziz Butt Naz, Sumera Javier, Jimènez-Cabas Gabriel, Piñeres-Espitia Department of Computer Science and Electronics Universidad de La Costa Calle 58 55-66 Atlántico Barranquilla 080002 Colombia Department of Applied Mathematics Ayandegan Institute of Higher Education Ferdowsi St RVF6+JJ9 Mazandaran Tonekabon *** Iran Deparment of Computer Science University of South Asia 47-Tufail Road Punjab Lahore 110021 Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

The manuscript presents new methodologies for process characterization and control tuning for integrated systems. Integrative systems are widespread in the industrial environment. Some control systems for level, pressure, concentration, and temperature are examples. Although many papers have been published on the subject, no standard has been established, so this remains an open problem. The proposed characterization method consists of deriving the response of the integrating system and applying characterization techniques for self-regulated processes. The proposed tuning method is based on deducing tuning equations for a PD control from the λ tuning rules. The performance of the proposed methods is validated through simulation and experimental tests. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Integrating process Process characterization/identification λ tuning

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Robustness of community structure under edge addition

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Physical Review E 2023年第5期108卷 054302-054302页

作者： Moyi Tian Pablo Moriano Division of Applied Mathematics Brown University Providence Rhode Island 02912 USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge Tennessee 37830 USA

Communities often represent key structural and functional clusters in networks. To preserve such communities, it is important to understand their robustness under network perturbations. Previous work in community robustness analysis has focused on studying changes in the community structure as a response of edge rewiring and node or edge removal. However, the impact of increasing connectivity on the robustness of communities in networked systems is relatively unexplored. Studying the limits of community robustness under edge addition is crucial to better understanding the cases in which density expands or false edges erroneously appear. In this paper, we analyze the effect of edge addition on community robustness in synthetic and empirical temporal networks. We study two scenarios of edge addition: random and targeted. We use four community detection algorithms, Infomap, Label Propagation, Leiden, and Louvain, and demonstrate the results in community similarity metrics. The experiments on synthetic networks show that communities are more robust when the initial partition is stronger or the edge addition is random, and the experiments on empirical data also indicate that robustness performance can be affected by the community similarity metric. Overall, our results suggest that the communities identified by the different types of community detection algorithms exhibit different levels of robustness, and so the robustness of communities depends strongly on the choice of detection method.

关键词： Community structure Network evolution Robustness

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